Each year GovLoop releases an updated guide to big data as data centers and solutions become a more entrenched aspect of best business practices for the government sector. The focus of the magazine has been and continues to be big data and how government agencies can more effectively implement efficient, streamlined data storage and aggregation.
In this year’s magazine, just released, Christopher Steel, Chief Solutions Architect at Software AG Government Solutions, discusses the importance of data aggregation and real-time analysis. Ultimately, the piece goes on to explain that within federal agencies, a great deal of their data is stored across several different information silos and many in different formats. Having data spread across so many sources decreases an agencies’ ability to operate at their highest capacity.
Consequently, when dealing with such large amounts of data as the government needs to, their means of storing, accessing and processing information have to change and operate more efficiently. Steel shares that there are two types of data: static and streaming. And, he goes on to explain that government agencies have to be able to run analysis on both types in order to get a clear picture of historical and real-time trends.
Steel goes on to contend; “Organizations should be able to take data that’s in one form and convert it to a form that’s needed, and share that data in real-time.” Because of the sheer amount of data that government agencies have to juggle, in order to be as efficient as possible, agencies have to be adept at gleaning value from static data and then it needs to be merged with real-time data to offer up a comprehensive analysis. Steel suggests that static data be stored in a solution platform like Hadoop.
Additionally, within the data guide, Steel goes on to explain that “If you have a Hadoop solution where you’re running analysis on an hourly, daily, weekly basis, what Software AG can bring to the table is the ability to augment those analysis with real-time data. We can color that data with contextual information from real-time feeds.”
What Steel suggests is that with improved analysis and aggregation, agencies will be able to more proficiently situate real-time data contextually and will ultimately improve upon interpreting historical data in order to measure it against real-time data so that the time it takes to identify issues of inefficiency are quicker and more effective.
If you would like to read more about how your agency can improve functionality and proficiency with data solutions, click here.